WG 6: Health Care, Medical Technology, Care
Self-learning systems promise a great potential for the health care system. Prevention, diagnosis and therapy are improved through the use of intelligently linked patient data. Medical assistance systems will help to recognise diseases at an early stage. They will also assist during surgery. In the care sector, digital assistances can help lifting and moving older patients. It is not the goal of medical AI-applications to replace skilled workers. Instead, the new technology wants to unburden and to support as best as possible. At the same time, the health care system is confronted with several important social questions. For instance, how safe is the personal health data being protected? Who is liable when the AI misdiagnosis?
Topics and Organisation of the Working Group
The Working Group deals with the options provided by self-learning systems for prevention, diagnosis and treatment in the medical field, in long-term care and rehabilitation. It also addresses questions of social acceptance and data protection.
Members of the Working Group
- Prof. Dr. Roland Eils
- Berlin Institute of Health (BIH)
- Prof. Dr. med. Steven Hildemann
- Merck KGaA
- Dr. Elsa Kirchner
- German Research Center for Artificial Intelligence (DFKI)
- Stefan Kley
- GRB Gesellschaft für Risiko-Beratung mbH
- Prof. Prof. h. c. Dr. med. Thomas Lenarz
- Hannover Medical School (MHH)
- Dr. Hans-Otto Maier
- B. Braun Melsungen AG
- Hardy Müller
- Aktionsbündnis Patientensicherheit
- Prof. Dr. Thomas Neumuth
- Leipzig University
- Dr.-Ing. Matthieu-P. Schapranow
- Hasso Plattner Institute for Digital Engineering
- Eva Maria Welskop-Deffaa
- Deutscher Caritasverband e.V.
- Prof. Dr. Karin Wolf-Ostermann
- University of Bremen
- Prof. Dr. Ing. Thomas P. Zahn
- Gesundheitswissenschaftliches Institut Nordost (GeWINO)
- Christian Zapf
- Siemens Healthineers AG
We have received written consent from the listed persons for the publication of their data in accordance with the DSGVO. This list of members is an excerpt and will be completed continuously.
Key Questions for the Working Group
- Which opportunities occur for self-learning systems when patient, clinical and wearable data is connected with each other?
- How can we use Data Science and AI for prevention approaches?
- What are the opportunities for self-learning assistant systems during operations and in the care sector?
- Which role do AI-technologies play when it comes to prosthetics and exoskeletons?
- How can self-learning systems relieve skilled personal in health care?
Conclusions and Contributions of the Working Group
- Self-Learning Systems in the Healthcare System
- Year of publication: 2019
- Whether in prevention, early diagnosis or selecting the ideal treatment, Artificial Intelligence (AI) and Machine Learning (ML) could soon be playing a big part in ensuring that people receive better and more personalised medical care. There is a whole variety of ways that self-learning systems could be put to use in the medical practices and hospitals of the future. For instance, doctors could use AI systems across the board to help them evaluate imaging procedures, thereby obtaining more accurate diagnoses. By using networked data, self-learning systems could derive proposals for suitable preventive approaches or treatments, thus helping medics and patients make important decisions.
- Contact: Birgit Obermeier / Linda Treugut
Coordination of the Working Group at the Managing Office: Dr. Thomas Schmidt